August Personalization Round up

By Dan Darnell on August 29, 2012

The Personalization Roundup is an ongoing blog series featuring the most evocative news, articles and blogs curated by Baynote each month highlighting the rapidly growing topic of web personalization. For this month’s roundup, we’ve combed the web to bring you the latest and greatest on the personalization revolution.

Over the last month we saw everything from articles on the pitfalls of big data to surveys comparing the effectiveness of email, search and social marketing campaigns. Taken together, this collection of articles demonstrates that retailers are realizing the value of eCommerce personalization even if they are having trouble mastering it. Read on to learn more about the myth of one-to-one personalization, how brick-and-mortar retailers are emulating retail personalization and the big-data “sins” you should avoid.

“The Psychology of Personalization: Needs, Wants and Influence,” CMSWire – Baynote’s very own CTO and Cofounder, Dr. Scott Brave, continues to influence the ecommerce-personalization conversation with this article discussing his recent white paper, “The Human Need for Personalization: Psychology, Technology and Science.” The article tackles the myth of one-to-one personalization, discusses the need to influence consumer behavior instead of predicting it, and highlights the role of personalization inputs, outputs and dimensions. The article concludes with a Baynote infographic detailing how leading personalization sites collect and use your personal data. If you haven’t already seen Scott’s paper, you can download it here.

“Email Marketing Converts Better Than Search, Social Media, Says Study,” MediaBistro – With social media being the talk of the town, it’s easy to forget about more traditional customer engagement strategies. In this article, MediaBistro clearly demonstrates that while social media is important, email marketing and search campaigns lead to more conversions. In fact, average conversion rates for email marketing were over 1.5 times greater than search, and search was 5.5 times greater than social! The major takeaway: use all three to optimize your marketing efforts.

“Retailers’ Ideas: Think Smaller in Urban Push,” The New York Times – While this article does not discuss ecommerce, it did make us realize just how much brick-and-mortar stores can learn from ecommerce’s personalization efforts. The article describes how large retailers such as Office Depot, Wal-Mart and Target are moving into urban areas with smaller, more targeted stores. To ensure those stores only carry items suited for urban dwellers, the retailers studied the needs of residents living around the stores and stocked items accordingly. Couple this strategy with the myth of one-to-one personalization, and these large retailers could be on to something!

“Tales for eTail Boston,” Business 2 Community – Retailers from around the country met in Boston this month to discuss the latest ecommerce trends and tools. Not surprisingly, personalization was a hot topic! More specifically, the need to deliver personalized content and understand the common barriers to doing so effectively. Barriers discussed focused on three may areas: lack of resources, choosing the wrong vendor and the lack of a big data strategy. Of those in attendance, only three percent were confident that they had already found the right solution for creating personalized web experiences. If you are part of the other 97 percent, don’t hesitate to give us a call!

“7 Deadly Sins of Big Data Users,” Information Week – This article by Josh Williams, president and chief science officer of Kontagent, argues that big data can give companies a significant competitive advantage, but only if they they approach it correctly. To illustrate common big-data pitfalls, Josh describes the “7 Big Data User Sins” as sloth, negligence, gluttony, polemy, imprudence, pride and torpor. Those sins align with lazy data collection, misapplied analysis, too many reports, data definition and use disagreements, jumping to conclusions, decision-driven data making, and learning and acting slowly.